Advancements in computing devices and user identification technology have led to a variety of innovations in providing personalized digital content to client devices of individual users. For example, user identification systems are now able to analyze a digital profile of a user to identify content that interests the particular user and then transmit the digital content as part of digital interactions with client devices of the user.
Despite these advances however, conventional user identification systems continue to suffer from a number of disadvantages, particularly in the efficiency, accuracy, and flexibility of identifying client devices corresponding to individuals in providing customized digital content. For instance, while conventional user identification systems can identify digital content that might interest a given user, these systems often require large amounts of computer memory and other computing resources to identify client devices corresponding to the user. Indeed, conventional user identification systems inefficiently analyze all (or nearly all) available user profiles to identify a target user, requiring significant memory and processing power. The time and processing power required by these conventional systems is particularly problematic in light of the need to provide digital content across computing systems in real-time (i.e., near instantaneously) in many circumstances.
In addition, conventional user identification systems are often inaccurate. More specifically, conventional user identification systems often misclassify client devices of individual users and thus provide incorrect digital content. This is a particularly significant problem because data regarding individual users are available and recorded in a variety of places all over digital space (including across various devices and channels utilized to access content via the Internet). Accordingly, different users have different information profiles and different amounts of information to utilize in classifying target users. Accordingly, the accuracy of predictions can vary widely with variability in available data utilized to generate predictions within conventional user identification systems.
Moreover, many conventional user identification systems are inflexible. Indeed, given the accuracy problems just discussed, some user identification systems only provide targeted digital content to users that are definitively identified (e.g., based on a known IP address or known log-in information). Such systems thus rigidly provide customized digital content to known users but fail to flexibly identify and provide digital content for users based on available digital characteristics.
Thus, there are several disadvantages with regard to conventional user identification systems.